In the ongoing quest toward aiding artificial intelligence (AI)-based decision-making with explainable AI (XAI), it is not just the explanations themselves that matter. Decisions about when to provide explanations or when to provide the recommendation for any decision have in recent years been observed to be just as vital for the successful use of AI systems for decision-making. Because providing the recommendation first is frequently associated with confirmation bias and overreliance, researchers have developed a variety of interaction protocols that promise better results in different situations, depending, e.g., on the performance of human or AI alone. Interaction protocols can mandate complete delegation to the AI, or even involvement of a third party to provide remedy in the face of disagreement. 

Therefore, in the scope of this seminar paper, a literature review will be conducted to explore how different interaction protocols are discussed in the XAI literature, how they are motivated, implemented, and evaluated. 

Literatur

• Gomez, C., Cho, S. M., Ke, S., Huang, C. M., & Unberath, M. (2025). Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review. Frontiers in Computer Science, 6, 1521066.
• Cabitza, F., Campagner, A., Fregosi, C., Cameli, M., Gallazzi, E., Sconfienza, L. M., & Tontini, G. E. (2025). Five Degrees of Separation: Investigating the Unexpected Potential of Displaced Human-AI Collaboration Protocols for Apter AI Support. Proceedings of the ACM on Human-Computer Interaction, 9(7), 1-28.

The use of explainable artificial intelligence (XAI) has led to performance gains reported in human-artificial intelligence (AI) collaboration throughout various domains and use cases. In recent years however, researchers have started to attribute this observation not to an increased understanding of the AI system, but instead to a human tendency to rely too much on AI-generated recommendations. With AI usually outperforming humans in the aforementioned studies, performance increases would have manifested merely due to humans being more inclined to trust and follow AI recommendations when accompanied by XAI. This also leads to the problem of performance becoming better only with respect to the human on their own, not surpassing the AI system on its own to reach complementary performance.

In the scope of this seminar paper, a literature review will be conducted to explore the state of the art of XAI studies measuring reliance behavior and aiming to mitigate the negative effects of inappropriate levels of trust and reliance.

Literatur

• Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8(12), 2293-2303.
• Schoeffer, J., Jakubik, J., Vössing, M., Kühl, N., & Satzger, G. (2025). AI reliance and decision quality: Fundamentals, interdependence, and the effects of interventions. Journal of Artificial Intelligence Research, 82, 471-501.

Adaptive systems are designed to assess a user’s current state and support their activities by dynamically adapting to it. Such a system can offer benefits for individuals across different domains by providing new forms of human-computer interaction and improving user experience. For example, a system that automatically suppresses task-irrelevant notifications when it detects that a student is in a state of high focus could boost learning efficiency and academic performance. However, the effectiveness of adaptive systems largely depends on the design and selection of appropriate adaptation strategies. Therefore, this systematic review aims to provide an overview of current adaptation strategies for adaptive-systems in the context of knowledge workers.

Literatur

• Ferreira, S., Rodrigues, M. A., Mateus, C., Rodrigues, P. P., & Rocha, N. B. (2025). Interventions based on biofeedback systems to improve workers’ psychological well-being, mental health, and safety: systematic literature review. Journal of Medical Internet Research, 27, e70134.
• Loewe, Nico and Nadj, Mario, "Physio-Adaptive Systems- A State-of-the-Art Review and Future Research Directions" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020. 
  aisel.aisnet.org/ecis2020_rp/47
• Paramythis, A., Weibelzahl, S., & Masthoff, J. (2010). Layered evaluation of interactive adaptive systems: framework and formative methods. User Modeling and User-Adapted Interaction, 20(5), 383-453.

Mit der zunehmenden Integration generativer und agentischer künstlicher Intelligenz in betriebliche Arbeitsprozesse verändern sich nicht nur die Art der Aufgabenerledigung, sondern auch die sozialen Dynamiken innerhalb von Teams. Während die bisherige Forschung vor allem Produktivitätsgewinne dokumentiert hat, wurden die sozialen Folgen der KI-Integration in Teams bislang kaum beleuchtet. Erste Befunde deuten darauf hin, dass eine intensive Nutzung von KI zwischenmenschliche Interaktionen im Arbeitsalltag verringert, bestehende Teamstrukturen verändert und neue Ungleichgewichte in Bezug auf Kompetenz, Einfluss und Rollenverteilung erzeugt. Mit dem Aufkommen von KI, die eigenständig Aufgaben übernimmt und Entscheidungen vorbereitet, stellt sich zudem die Frage, wie sich Teamrollen und Verantwortlichkeiten weiter verändern werden. Bislang bleibt jedoch weitgehend ungeklärt, wie sich diese Prozesse im Teamalltag konkret entfalten und unter welchen Bedingungen KI menschliche Interaktion ersetzt statt ergänzt. Diese Seminararbeit untersucht die genannten negativen Auswirkungen mittels einer qualitativen Interviewstudie. Mithilfe semistrukturierter Interviews mit Beschäftigten, die regelmäßig generative oder argentinische KI im Teamkontext nutzen, soll analysiert werden, wie sich Interaktionsmuster, Rollenverteilungen und Machtdynamiken durch die KI-Integration verändern und welche organisationalen Rahmenbedingungen diese Effekte verstärken oder abmildern.

Literatur

• Duan, W., Flathmann, C., McNeese, N., Scalia, M. J., Zhang, R., Gorman, J., Freeman, G., Zhou, S., Hauptman, A. I. & Yin, X. (2025). Trusting Autonomous Teammates in Human-AI Teams - A literature review. CHI Conference On Human Factors in Computing Systems, 1–23. doi.org/10.1145/3706598.3713527
• Naikar, N., Hoffman, R., Roth, E. M., Klein, G., Militello, L. G. & Dominguez, C. (2025). Should we Make AI More Tool-like or Teammate-Like? Journal Of Cognitive Engineering And Decision Making. doi.org/10.1177/15553434251346904
• Tu, Y., Li, J., Chen, J., Li, C. & He, W. (2025). When AI Becomes My Teammate: Unpacking How Employees Perceive and Collaborate With Gendered AI Teammates. Journal Of Organizational Behavior. doi.org/10.1002/job.70038
• Winter, J. (2025). AI teammates and human performance: Evidence for commitment deficits. Computers in Human Behavior Reports, 20, 100828. https://doi.org/10.1016/j.chbr.2025.100828

Enterprise systems and its component, such as Enterprise-Resource-Planning, Customer-Relationship-Management or Supply-Chain-Management Systems are largely based on software platforms provided by vendors such as SAP, Oracle, or Microsoft (Schütte & Kari, 2025). These components support integrated business processes, ensure transactional reliability, and enable consistent data management across organizational functions. Their design traditionally emphasizes stability, standardization, and control (Schütte, 2017). With the increasing importance of Artificial Intelligence (AI), vendors are embedding AI capabilities into their systems to enable predictive insights, intelligent automation, and more adaptive decision support. Integrating AI into enterprise systems, however, introduces new architectural and organizational requirements. AI components rely on probabilistic models, large data volumes, and continuous learning processes, which challenge traditional principles of determinism, transparency, and system governance (Sarferaz, 2025). Academic research highlights the need for a new architecture of enterprise systems, enhanced data governance practices, and reliability in AI-enabled systems. Simultaneously, enterprise software vendors present AI as a core element of next-generation enterprise platforms, emphasizing seamless integration, enhanced automation, and improved decision-making capabilities. A structured literature review should be conducted to systematically identify and synthesize the architectural and organizational requirements discussed in academic research regarding the integration of AI into enterprise systems. In addition, vendor communications (e.g., website, product announcements) should be analyzed to examine how leading enterprise systems vendors position AI within their platforms and articulate its value and implementation. By comparing insights from scientific literature with vendor narratives, the analysis should identify areas of alignment and divergence and derive implications for organizations seeking to adopt AI-enabled enterprise systems.

Literatur

• Sarferaz, S. (2025). Implementing Agentic AI into ERP software. IEEE Access, 13, 178945–178960. https://doi.org/10.1109/access.2025.3621887
• Schütte, R. (2017). Immer mehr Standard: Cloud Computing im Kontext von Enterprise Systems. DuEPublico (University of Duisburg-Essen), 108–115. https://doi.org/10.17185/duepublico/70382
• Schütte, R., & Kari, M. (2025). Cloud Enterprise Systems – State of the Art und Herausforderungen für Unternehmen. HMD Praxis Der Wirtschaftsinformatik, 62(1), 5–24. https://doi.org/10.1365/s40702-025-01141-3

“Vibe Coding” – a “software development paradigm where human and generative artificial intelligence (GenAI) engage in a collaborative flow to co-create software artifacts through natural language dialogue” (Meske et al., 2025, p. 213243) – represents a fundamental transition from deterministic instruction (where developers explicitly encode intent through formal syntax) to probabilistic inference (where GenAI systems infer meaning from naturalistic expression). Under this paradigm, the professional identity of the developer shifts from manual line-level authorship to the holistic orchestration of computational intent, wherein the human acts as kind of a “creative director” overseeing autonomous (sometimes multi-agent) GenAI systems (Hughes et al., 2025). In both research and practice, vibe coding becomes increasingly relevant. Vibe Coding platforms, such as Lovable, Google’s Opal, Cursor, or Windsurf have gained increasing attention from developers worldwide. First, vibe coding enables the democratization of software production and accelerates development cycles from months to days, as also shown by industrial reports demonstrating that substantial portions of emerging startup codebases are AI-generated (e.g., Mehta, 2025) and that approximately 40% of new enterprise software will be created via vibe coding in 2028 (e.g., Gartner, 2025). At the same time, early evidence and practitioner discourse indicate that vibe coding can introduce new risks and trade-offs that may be less visible. For instance, stackoverflow.com banned the use of GenAI for posts and GitHub disabled pull requests for GenAI systems to avoid having to look at AI slop. Despite growing attention, academic understanding of vibe coding is still emerging, while practitioner interpretations and norms are forming in real time. A focused social media analysis of experienced developers’ discussions provides a timely, naturalistic lens into how the phenomenon is interpreted “on the ground” (e.g., benefits, fears, boundary conditions, and emerging norms). The subreddit r/ExperiencedDevs is especially suitable because it is explicitly oriented toward experienced practitioners and contains rich, argument-driven discussion threads relevant to professional software engineering practices.

The seminar paper aims to identify experienced developers’ opinions and dominant themes regarding vibe coding by conducting a social media analysis of discussions on r/ExperiencedDevs. Methodologically, the study can follow the established Social Media Analytics (SMA) process, covering discovery, data collection, data preparation, and analysis (see Stieglitz et al., 2018). For qualitative analysis, the seminar may adopt the method by Gioia et al. (2013) to build a transparent, grounded “data structure” of how experienced developers frame vibe coding.

An Excel-File with scraped Threads and Comments from r/ExperiencedDevs will be provided for the student for further analysis. We recommend using the software MAXQDA for coding, which is provided for free by the University.

Literatur

• Gartner (2025). Why Vibe Coding Needs to Be Taken Seriously. Accessed: March. 03, 2026. [Online]. Available: info.legitsecurity.com/gartner-vibe-coding-report
• Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, 16(1), 15-31.
• Hughes, L., Dwivedi, Y. K., Malik, T., Shawosh, M., Albashrawi, M. A., Jeon, I., ... & Walton, P. (2025). AI agents and agentic systems: A multi-expert analysis. Journal of Computer Information Systems, 65(4), 489-517.
• Mehta, I. (2025). A Quarter of Startups in YC’s Current Cohort Have Codebases That Are Almost Entirely AI-Generated. Accessed: March. 03, 2026. [Online]. Available: techcrunch.com/2025/03/06/a-quarter-of-startups-in-ycs-current-cohort-have-codebases-that-are-almost-entirely-ai-generated/
• Meske, C., Hermanns, T., Von der Weiden, E., Loser, K. U., & Berger, T. (2025). Vibe coding as a reconfiguration of intent mediation in software development: Definition, implications, and research agenda. IEEE Access, 13, 213242-213259.
• Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.

Generative künstliche Intelligenz (GenAI), insbesondere Large Language Models (LLMs), die in Chatbots, Schreib- und Codingtools verfügbar sind (z. B. Copilot, ChatGPT, NotebookLM), werden zunehmend nicht nur von Unternehmen sondern insbesondere von Schülern und Studierenden eingesetzt. GenAI hat sich dabei in den letzten Jahren zu einem routinierten Partner im täglichen universitären Leben etabliert (vgl. Park, 2025). In unseren Modulen, wie beispielsweise Enterprise Transformation, aber auch bei Seminararbeiten, Projektarbeiten und Abschlussarbeiten nehmen wir immer stärker wahr, dass Studierende für Ihr Studium GenAI einsetzen. Entsprechend rückt in der Wirtschaftsinformatiklehre weniger die Frage in den Vordergrund, ob Studierende GenAI nutzen, sondern wie sie diese Systeme in Lernprozesse integrieren, welche Fähigkeiten dafür erforderlich sind und inwiefern die Studierenden hinsichtlich des Einsatzes von GenAI sensibilisiert sind (Van Slyke et al., 2023). GenAI zeichnet sich durch eine Vielzahl an Fähigkeiten aus (vgl. Harr et al., 2024), die im Studium Chancen (z. B. individuelle/personalisierte Unterstützung, schnellere Iterationen) aber auch Risiken (z. B. plausible, aber falsche Inhalte, Abhängigkeit, Integritäts- und Qualitätsprobleme) zugleich verstärken können; was sich wiederum in neuen didaktischen Herausforderungen ausdrückt (siehe Gimpel et al., 2025). Die Nutzung von GenAI ist nicht per se problematisch und an der Universität Duisburg-Essen prinzipiell erlaubt, kann aber bei fehlender Sensibilisierung zu problematischen Fehlanwendungen führen; etwa durch ungeprüfte Übernahme halluzinierter Inhalte.

Ziel des Seminars ist deshalb – basierend auf semi-strukturierten Interviews (vgl. Übersicht von Iyamu, 2018) – den Status quo der Sensibilisierung von Masterstudierenden der Wirtschaftsinformatik, Betriebswirtschaftslehre, und Informatikbeim Einsatz von GenAI im Studium systematisch zu erfassen. Basierend auf reichhaltigen Erkenntnissen der Interviews können so Implikationen für die Hochschullehre und Unterstützungsangebote abgeleitet werden (z. B. Schulungen, Trainings, andere Aufgaben- und Klausurformate, etc.).

Literatur

• Gimpel, H., Hall, K., Decker, S., Eymann, T., Gutheil, N., Lämmermann, L., Braig, N., Maedche, A., Röglinger, M., Ruiner, C., Manfred Schoch, Schoop, M., Urbach, N., & Vandirk, S. (2025). Using Generative AI in Higher Education: A Guide for Instructors. Journal of Information Systems Education, 36(3), 237-256. https://doi.org/10.62273/QLLG7172
• Harr, M. D., Wienand, M., & Schütte, R. (2024). Towards Enhanced E-Learning Within MOOCs: Exploring the Capabilities of Generative Artificial Intelligence. In Proceedings of the Pacific-Asia Conference on Information Systems.
• Iyamu, T. (2018). Collecting qualitative data for information systems studies: The reality in practice. Education and Information Technologies, 23(5), 2249-2264.
• Park, J. (2025). A systematic literature review of generative artificial intelligence (GenAI) literacy in schools. Computers and Education: Artificial Intelligence, 100487.
• Van Slyke, C., Johnson, R. D., & Sarabadani, J. (2023). Generative artificial intelligence in information systems education: Challenges, consequences, and responses. Communications of the Association for Information Systems, 53(1), 1-21.

Artificial Intelligence (AI) is a broad field of computer science that focuses on developing systems capable of performing tasks traditionally associated with human intelligence, including perception, reasoning, learning, and problem-solving (Russell, 2016). A rapidly emerging subfield is Generative Artificial Intelligence (GenAI), which aims to generate new content such as text, images, audio, or software code by learning patterns from large-scale datasets (Feuerriegel et al., 2023). In contrast to traditional AI systems that primarily perform classification or prediction tasks, GenAI systems are able to produce novel outputs that often resemble human-created artifacts (Feuerriegel et al., 2023). This generative capability has led to increasing attention in both academic research and business practice.

The potential applications of GenAI are diverse and continue to expand. For example, it can be used for the automated generation of product descriptions (Ghaffari et al., 2024) or to support the development of new business models (Kanbach et al., 2024). These use cases illustrate that GenAI can improve efficiency and enable new forms of value creation within organizations.

Despite this potential, the question of how GenAI contributes to IT business value, defined as the benefits organizations derive from IT investments (Schryen, 2013), remains unresolved. It is still unclear under which conditions GenAI technologies create measurable organizational value. Therefore, empirical research (e.g. Interviews following Rowley, 2012) is needed to identify the factors that influence GenAI-driven value creation within organizations. One possible theoretical lens for such an analysis is the TOE framework (Tornatzky and Fleischer, 1990), which examines technological, organizational, and environmental factors affecting technology adoption and impact.

Literatur

• Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. doi.org/10.1007/s12599-023-00834-7
• Ghaffari, S., Yousefimehr, B., & Ghatee, M. (2024). Generative-AI in E-Commerce: Use-Cases and Implementations. 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 1–5. doi.org/10.1109/aisp61396.2024.10475266
• Kanbach, D. K., Heiduk, L., Blueher, G., Schreiter, M., & Lahmann, A. (2023). The GenAI is out of the bottle: generative artificial intelligence from a business model innovation perspective. Review of Managerial Science, 18(4), 1189–1220. https://doi.org/10.1007/s11846-023-00696-z
• Rowley, J. (2012). Conducting research interviews. Management Research Review, 35(3/4), 260–271. doi.org/10.1108/01409171211210154
• Russell, S. (2016). Artificial intelligence (P. Norvig, Ed.; Third edition.). Pearson.
• Schryen, G. (2013). Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22(2), 139–169. https://doi.org/10.1057/ejis.2012.45
• Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington, MA: Lexington Books.

Das IT-Consulting befindet sich im Wandel: Die zunehmende Verfügbarkeit und Leistungsfähigkeit von Künstlicher Intelligenz verändert die Art und Weise, wie Beratungsleistungen erbracht werden. Klassische Tätigkeiten wie die Analyse von Systemarchitekturen, die Optimierung von Geschäftsprozessen oder die Erstellung von Migrationsszenarien und der Programmierung selbst beruhten bisher stark auf menschlicher Expertise. Mit KI-gestützten Anwendungen eröffnen sich neue Möglichkeiten – von der automatisierten Datenanalyse über die Simulation komplexer Szenarien bis hin zur Generierung von Handlungsempfehlungen (Davenport & Miller, 2022).

Dabei entsteht ein Spannungsfeld zwischen Effizienzsteigerung durch Automatisierung und der weiterhin unverzichtbaren menschlichen Beratungskompetenz. KI kann Beraterinnen und Berater unterstützen, Routineaufgaben zu reduzieren, schneller zu fundierten Ergebnissen zu gelangen und Entscheidungsprozesse datenbasiert zu untermauern. Gleichzeitig wirft der Einsatz von KI die Frage auf, wie sich Rollenprofile, Wertschöpfungsmodelle und das Verhältnis von Mensch und Maschine im Consulting langfristig verändern werden.

Ziel dieser Seminararbeit ist es, auf Grundlage einer strukturierten Literaturrecherche zukünftige Anwendungsfelder von KI im IT-Consulting zu identifizieren. Darüber hinaus sollen Chancen und Risiken des KI-Einsatzes für Beratungsunternehmen sowie die Implikationen für Beratungsprozesse und Rollenprofile aufgearbeitet und kritisch reflektiert werden.

Literatur

• Davenport, T. H., & Miller, S. M. (2022). Working with AI: real stories of human-machine collaboration. MIT Press.
• Bezuidenhout, C., Abbas, R., Mehmet, M. & Heffernan, T. (2025). Artificial Intelligence in Professional Services: A Systematic Review and Foundational Baseline for Future Research. Journal Of Information & Knowledge Management. https://doi.org/10.1142/s0219649225500091
• Tredinnick, L. (2017). Artificial intelligence and professional roles. Business Information Review, 34(1), 37-41. https://doi.org/10.1177/0266382117692621

Large Language Models (LLMs) can serve as the foundation for assistance systems that enhance personal productivity by automating routine tasks, summarizing information, drafting communications, managing schedules, and providing decision support. By understanding natural language input, LLM-based assistants can interact seamlessly with users, integrate with applications, and adapt to individual workflows. These systems have the potential to increase efficiency, reduce cognitive load, and improve time and knowledge management, while raising important considerations regarding reliability, privacy, and user trust.

The student’s task is to explore how LLMs can be applied in assistance systems, with a focus on personal productivity scenarios. This includes analyzing design approaches, integration strategies, and practical use cases, as well as evaluating benefits, limitations, and potential risks. The paper should provide a structured assessment of LLMs as tools for enhancing workflow management, either through a systematic literature review or a design science approach that evaluates and proposes conceptual models or prototype implementations.

This paper is designed as a structured review or applied design study. The central research question is: How can Large Language Models be effectively utilized in assistance systems to support personal productivity, and what are their benefits, limitations, and potential risks? By systematically reviewing academic and practitioner literature or applying a design science methodology, the study should provide a conceptually grounded understanding of LLM-enabled productivity tools.

Literatur

• Möhring, M., Keller, B., & Bierhals, D. (2024, June). The usage of language models for human assistance in production failure root cause analysis. In International KES Conference on Human Centred Intelligent Systems (pp. 39-49). Singapore: Springer Nature Singapore.
• Perumalla, V. R., Koppolu, S. S., Pashikanti, S. S., Alluri, S. V., Dantala, S. K., Yanamadni, V. R., ... & Nanda, S. (2025, August). AI intelligent helper employing a large language model. In AIP Conference Proceedings (Vol. 3263, No. 1, p. 020002). AIP Publishing LLC.
• Weber, T., Brandmaier, M., Schmidt, A., & Mayer, S. (2024). Significant productivity gains through programming with large language models. Proceedings of the ACM on Human-Computer Interaction, 8(EICS), 1-29.
• Zheng, M., Pei, J., Logeswaran, L., Lee, M., & Jurgens, D. (2024, November). When” a helpful assistant” is not really helpful: Personas in system prompts do not improve performances of large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 15126-15154).

Large Language Models (LLMs) operate within complex data ecosystems, relying on diverse and extensive datasets for pretraining, fine-tuning, and continuous performance improvement. These ecosystems include structured and unstructured data sources, knowledge bases, APIs, and user-generated content, all of which influence model capabilities, accuracy, and bias. Effective use of LLMs requires robust data governance practices, including quality control, compliance, privacy, and ethical oversight. Well-designed data pipelines and integration strategies are essential for ensuring reliable, secure, and auditable model performance in organizational and research contexts.

The student’s task is to examine the interaction between LLMs and data ecosystems, with a particular focus on data governance and pipeline design. This includes analyzing data sources, management strategies, integration approaches, and governance frameworks, as well as assessing their impact on model reliability, performance, and ethical use. The paper should provide a structured overview of how data ecosystems shape the functionality, deployment, and responsible application of LLMs in research, business, and knowledge-intensive workflows.

This paper is designed as a systematic literature review. The central research question is: How do data ecosystems, governance frameworks, and pipeline architectures influence the performance, reliability, and responsible use of Large Language Models? By systematically reviewing academic and practitioner literature, the study should provide a conceptually grounded understanding of the relationship between LLMs and their underlying data environments.

Literatur

• Cozzini, S., & de Luca, M. (2024). Living in Digital Ecosystems: Are We Aware of This?. In Digital Environments and Human Relations: Ethical Perspectives on AI Issues (pp. 113-132). Cham: Springer Nature Switzerland.
• Heinz, D., Benz, C., Fassnacht, M., & Satzger, G. (2022). Past, present and future of data ecosystems research: A systematic literature review.
• Luntovskyy, A., & Vasyutynskyy, V. (2024, February). Large Language Models and Digital Ecosystems in Business Scenarios. In IEEE lnternational Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (pp. 804-821). Cham: Springer Nature Switzerland.
• Symeonidis, D., & Nikiforova, A. (2025). Integrating Generative AI with Private Data Ecosystems: Enhancing Decision-Making and Efficiency in the Service Industry of the Private Sector.

Large Language Models (LLMs) can significantly enhance organizational benchmarking and maturity assessment by analyzing processes, capabilities, and strategic practices at scale. They are capable of processing large volumes of structured and unstructured data to identify best practices, generate comparative insights, and evaluate organizational or technological maturity. By automating data analysis and producing data-driven recommendations, LLMs can improve decision-making, reduce assessment effort, and enable continuous monitoring of progress across multiple domains.

The student’s task is to examine the application of LLMs for benchmarking and maturity assessment. This includes analyzing methods for data collection, processing, and comparison, evaluating the reliability and usefulness of AI-generated insights, and reviewing practical use cases. The paper should provide a structured assessment of how LLMs can support organizational evaluation, strategic decision-making, and continuous improvement initiatives.

This paper is designed as a systematic literature review. The central research question is: How can Large Language Models be effectively applied to organizational maturity assessment and benchmarking, and what are their strengths, limitations, and practical implications? By systematically reviewing academic and practitioner literature, the study should provide a conceptually grounded understanding of LLM-enabled organizational evaluation.

Literatur

• Hao, J., von Davier, A. A., Yaneva, V., Lottridge, S., von Davier, M., & Harris, D. J. (2024). Transforming assessment: The impacts and implications of large language models and generative AI. Educational Measurement: Issues and Practice, 43(2), 16-29.
• Hu, X., Niu, F., Chen, J., Zhou, X., Zhang, J., He, J., ... & Lo, D. (2025). Assessing and advancing benchmarks for evaluating large language models in software engineering tasks. ACM Transactions on Software Engineering and Methodology.
• Joshi, S. (2025). Evaluation of large language models: Review of metrics, applications, and methodologies.
• Romano, S. P., Sperli, G., & Vignali, A. (2024). An NLP-based approach to assessing a company’s maturity level in the digital era. Expert Systems with Applications, 252, 124292.

Prompt engineering is a key technique for improving the performance, reliability, and contextual relevance of Large Language Models (LLMs). By carefully designing input prompts and context structures, users can guide LLMs to produce accurate, coherent, and context-aware outputs while mitigating common weaknesses such as hallucinations, bias, or misinterpretation. Techniques include zero-shot and few-shot prompting, chain-of-thought reasoning, iterative refinement, and context augmentation, all aimed at enhancing model reasoning, clarity, and alignment with user objectives. Effective context engineering further ensures that LLM outputs remain relevant, reliable, and aligned with the intended domain or task.

The student’s task is to analyze approaches in prompt and context engineering for improving LLM reliability. This includes exploring various prompting strategies, evaluating their effectiveness across different scenarios, and examining practical examples of enhanced LLM performance. The paper should provide a structured assessment of how prompt and context engineering techniques can optimize LLM usability, accuracy, and applicability in research, business, and applied settings.

This paper is designed as a structured literature review. The central research question is: How can prompt and context engineering techniques be applied to enhance the reliability, accuracy, and task-alignment of Large Language Models? By systematically reviewing academic and practitioner literature, the study should provide a conceptually grounded understanding of methods for improving LLM performance and trustworthiness.

Literatur

• Arun, C., Gejjela, M., Bhadja, K., Anupama, C. G., Selvakumarasamy, S., & Gopinath, N. (2026). From Prompts to Contexts: Analysis of LLM's Strengths and Weaknesses. Power Engineering and Intelligent Systems: Proceedings of PEIS 2025, Volume 2, 2, 407.
• Chen, B., Zhang, Z., Langrené, N., & Zhu, S. (2025). Unleashing the potential of prompt engineering for large language models. Patterns, 6(6).
• Gupta, R., Tiwari, S., & Chaudhary, P. (2025). Prompt engineering. In Generative AI: Techniques, Models and Applications (pp. 163-186). Cham: Springer Nature Switzerland.
• Poola, I. (2023). Overcoming ChatGPTs inaccuracies with pre-trained AI prompt engineering sequencing process. International journal of technology and emerging sciences (ijtes), 3(3), 16-19.

Large Language Models (LLMs) offer substantial potential for logistics, particularly in complex environments such as airports, where information management, security, and operational coordination are critical. LLMs can support process automation, real-time data analysis, decision support, and knowledge management across multiple operational areas. Potential applications include optimizing passenger flow, managing baggage and cargo logistics, supporting security screening, generating reports, and assisting staff in information retrieval and communication. Integrating LLMs into these processes can improve efficiency, reduce errors, and enhance situational awareness in high-stakes operational contexts.

The student’s task is to analyze the application of LLMs in airport logistics, focusing on information management and security. This includes identifying key operational areas, mapping relevant processes, and assessing where LLMs can add value. For each process, the paper should evaluate potential benefits, limitations, and practical considerations. The study may employ an architecture proposal or process analysis to systematically illustrate how LLMs can be embedded into airport logistics and operational workflows.

This paper is designed as a structured literature review with applied process or system analysis. The central research question is: How can Large Language Models be effectively applied in airport logistics and information management to enhance operational efficiency, security, and decision support? By systematically reviewing academic and practitioner literature and analyzing process workflows, the study should provide a conceptually grounded understanding of LLM integration in complex logistical environments.

Literatur

• Sun, M., Tian, Y., Li, J., Wu, C. L., Peng, L., & Xu, S. (2025). A review of network delay prediction and advances in large language models for air traffic. Artificial Intelligence Review, 59(1), 36.
• Liu, Y. (2024). Large language models for air transportation: A critical review. Journal of the Air Transport Research Society, 2, 100024.
• Wang, L., Chou, J., Tien, A., Zhou, X., & Baumgartner, D. (2024). Aviationgpt: A large language model for the aviation domain. In AIAA AViation FOrum And AScend 2024 (p. 4250).
• Fox, K. L., Niewoehner, K. R., Rahmes, M., Wong, J., & Razdan, R. (2024, April). Leverage large language models for enhanced aviation safety. In 2024 Integrated Communications, Navigation and Surveillance Conference (ICNS) (pp. 1-11). IEEE.

Der demografische Wandel führt dazu, dass die Zahl älterer und pflegebedürftiger Menschen in vielen Ländern kontinuierlich steigt. Gleichzeitig steht die Altenpflege vor erheblichen Herausforderungen, darunter Fachkräftemangel, steigende Arbeitsbelastung für Pflegekräfte sowie eine zunehmende organisatorische Komplexität im Pflegealltag. Diese Herausforderungen betreffen sowohl stationäre Pflegeeinrichtungen als auch die ambulante Altenpflege, in der Pflegekräfte ältere Menschen in ihrem häuslichen Umfeld betreuen.

Technologische Innovationen – insbesondere im Bereich künstlicher Intelligenz, digitaler Assistenzsysteme und robotischer Unterstützung – werden daher zunehmend als mögliche Lösungen diskutiert, um Pflegekräfte zu entlasten und Pflegeprozesse zu unterstützen. Gleichzeitig stellt sich die Frage, welche technologischen Lösungen tatsächlich sinnvoll und akzeptabel im Kontext der Altenpflege sind. Pflegeeinrichtungen und private Haushalte sind nicht nur Arbeitsorte für Pflegekräfte, sondern auch Lebensräume für ältere Menschen. Technologien müssen daher sowohl praktische Arbeitsprozesse unterstützen als auch Aspekte wie Würde, Autonomie und menschliche Interaktion berücksichtigen.

Diese Masterseminararbeit untersucht daher, welche Probleme, Herausforderungen und Anforderungen im Alltag der Altenpflege bestehen und welche Designziele und Designanforderungen für zukünftige KI- und Robotiksysteme daraus abgeleitet werden können. Dabei werden sowohl stationäre Pflegeeinrichtungen als auch ambulante Pflegekontexte im häuslichen Umfeld älterer Menschen berücksichtigt.

Forschungsfragen:

• Welche typischen Arbeitsabläufe und Herausforderungen prägen den Alltag von Pflegekräften in der Altenpflege (stationär und ambulant)?
• Welche zentralen Probleme und Belastungen entstehen im Pflegealltag aus Sicht von Pflegekräften?
• Welche Technologien werden derzeit in der Altenpflege eingesetzt und wie werden diese bewertet?
• Welche Anforderungen und Erwartungen haben Pflegekräfte an zukünftige technologische Unterstützungssysteme, insbesondere im Bereich KI und Robotik?
• Welche Aspekte der Altenpflege können sinnvoll technologisch unterstützt werden und wo bestehen klare Grenzen für den Einsatz von Technologie?

Methodik:

Die Arbeit orientiert sich konzeptionell am Extended Design Science Research (eDSR) Ansatz (Tuunanen et al., 2024). Der eDSR-Ansatz strukturiert Design-Science-Forschung entlang mehrerer sogenannter Design-Echelons, die unterschiedliche Ebenen der Problem- und Lösungsentwicklung adressieren. Diese Arbeit konzentriert sich insbesondere auf Echelon 1 (Problem Exploration) und Echelon 2 (Derivation of Design Knowledge). Die weiteren Echelons des eDSR-Ansatzes, die sich mit der konkreten Entwicklung und Evaluation technischer Artefakte befassen, sind nicht Gegenstand dieser Arbeit.

Echelon 1 – Problem Exploration: In dieser Phase wird das Problemfeld systematisch untersucht, um ein fundiertes Verständnis der realen Praxisprobleme zu entwickeln. Ziel ist es, zentrale Herausforderungen, Arbeitsabläufe sowie bestehende Praktiken im Kontext der Altenpflege zu identifizieren.

Echelon 2 – Derivation of Design Knowledge: Auf Grundlage der identifizierten Problemfelder werden erste Designziele und Designanforderungen für potenzielle technologische Unterstützungssysteme in der Altenpflege abgeleitet. Dabei wird explizit berücksichtigt, dass nicht alle Herausforderungen technologisch adressierbar sind.

Zur Datensammlung werden qualitative Experteninterviews mit Pflegekräften aus der Altenpflege und/oder pflegebedürftigen Personen durchgeführt. Dabei können sowohl Pflegekräfte aus stationären Pflegeeinrichtungen als auch aus ambulanten Pflegediensten berücksichtigt werden. Ziel ist es, Einblicke in typische Arbeitsabläufe, Herausforderungen sowie Erfahrungen mit bestehenden Technologien zu gewinnen. Die Gestaltung und Durchführung der Interviews orientiert sich an etablierten Empfehlungen qualitativer Interviewforschung in der Informationssystemforschung (Myers & Newman, 2007).

Zur Datenauswertung werden die Interviews transkribiert und anschließend mittels induktivem Open Coding ausgewertet. Die Analyse orientiert sich methodisch an der Grounded Theory Methodology und dient der systematischen Identifikation zentraler Problemfelder und Anforderungen aus der Praxis (Birks et al., 2013).

Erwarteter Beitrag:

Die Arbeit liefert eine strukturierte und praxisnahe Übersicht zentraler Problemfelder in der Altenpflege aus Sicht von Pflegekräften aus stationären und ambulanten Pflegekontexten. Auf Basis dieser Problemübersicht werden Designziele und Designanforderungen für zukünftige KI- und Robotiksysteme im Kontext der Altenpflege abgeleitet.

Darüber hinaus trägt die Arbeit dazu bei, besser zu verstehen, welche Herausforderungen im Pflegealltag technologisch adressierbar sind und welche Aspekte menschliche Betreuung erfordern. Die Ergebnisse können damit als Grundlage für die Gestaltung zukünftiger technologischer Unterstützungssysteme in der Altenpflege dienen.

Literatur

• Statistisches Bundesamt. (2026). Pflege. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Pflege/_inhalt.html
• Gerling, K., Hebesberger, D., Dondrup, C., Körtner, T., & Hanheide, M. (2016). Robot deployment in long-term care. Zeitschrift Fur Gerontologie Und Geriatrie, 49, 288–297. https://doi.org/10.1007/s00391-016-1065-6
• Bratan, T., Schneider, D., Funer, F., Heyen, N. B., Klausen, A., Liedtke, W., Lipprandt, M., Salloch, S., & Langanke, M. (2024). Unterstützung ärztlicher und pflegerischer Tätigkeit durch KI: Handlungsempfehlungen für eine verantwortbare Gestaltung und Nutzung. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 67(9), 1039–1046. https://doi.org/10.1007/s00103-024-03918-1
• Zöllick, J. C., Rössle, S., Kluy, L., Kuhlmey, A., & Blüher, S. (2022). Potenziale und Herausforderungen von sozialen Robotern für Beziehungen älterer Menschen: Eine Bestandsaufnahme mittels „rapid review“. Zeitschrift Fur Gerontologie Und Geriatrie, 55(4), 298–304. https://doi.org/10.1007/s00391-021-01932-5
• Laufer, J., Banh, L., & Strobel, G. (2025). Bridging Mind and Matter: A Taxonomy of Embodied Generative AI. In AIS (Ed.), Wirtschaftsinformatik 2025 Proceedings.
• Strobel, G., Banh, L., Möller, F., & Schoormann, T. (2024). Exploring Generative Artificial Intelligence: A Taxonomy and Types. Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2023.546
• Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1
• Tuunanen, T., Winter, R., & Brocke, J. V. (2024). Dealing with Complexity in Design Science Research: A Methodology Using Design Echelons. MIS Quarterly, 48(2), 427–458. https://doi.org/10.25300/MISQ/2023/16700
• Myers, M. D., & Newman, M. (2007). The qualitative interview in IS research: Examining the craft. Information and Organization, 17(1), 2–26. https://doi.org/10.1016/j.infoandorg.2006.11.001
• Birks, D. F., Fernandez, W., Levina, N., & Nasirin, S. (2013). Grounded theory method in information systems research: Its nature, diversity and opportunities. European Journal of Information Systems, 22(1), 1–8. https://doi.org/10.1057/ejis.2012.48

Generative artificial intelligence (GenAI) leverages deep generative models to create novel content across domains such as text, images, video, and code based on simple user inputs (Banh & Strobel, 2023). In contrast to traditional AI systems, which primarily focus on prediction and pattern recognition, GenAI is capable of understanding context, learning from examples, and generating new content across domains (Wessel et al., 2025).

The emergence of GenAI represents a disruptive shift for digital platforms, fundamentally transforming how they operate and create value. Through the autonomous generation of outputs, GenAI introduces far-reaching implications for platform architecture, governance, and stakeholder interactions. In particular, GenAI platforms transform value creation through automation, democratization of participation, hyper-personalization, and human–AI collaboration, thereby increasing both scale and complexity.

Platform owners orchestrate ecosystems to enhance their value propositions (Kindermann et al., 2022). This orchestration is achieved through platform governance mechanisms, understood as activities that shape the functioning of the ecosystem (Chen et al., 2022; Rietveld & Schilling, 2021). Unlike traditional command-and-control approaches, these mechanisms rely on connect-and-coordinate logics to manage autonomous actors (Tilson et al., 2010).

In the context of GenAI, boundary resources and incentive structures must be adapted to integrate both human developers and agentic complementors. This includes, for example, agent-oriented interfaces, inter-agent protocols, generative APIs, and novel revenue models (Mayer et al., 2025).

At the same time, GenAI introduces specific risks such as hallucinations, jailbreaking, and challenges related to data quality and sensitive information, which require new governance mechanisms (Hein et al., 2020; Taeihagh, 2025). Furthermore, hyper-personalization and autonomous agents increase both value creation potential and regulatory requirements, particularly with regard to privacy, manipulation, and existing governance logics (Feuerriegel et al., 2024; Wessel et al., 2025).

Research Question:
Which governance mechanisms do operators of GenAI platforms implement to orchestrate value creation within their ecosystems?

Objective:
The objective of this thesis is the comprehensive development of a taxonomy of governance mechanisms in GenAI platform ecosystems. Methodologically, the study follows the approach of Nickerson et al. (2013), extended by Kundisch et al. (2022), and combines conceptual-to-empirical as well as empirical-to-conceptual iterations.

The first iteration is based on a structured overview of governance mechanisms provided by the chair, which was developed through a systematic literature review. Building on this foundation, five GenAI platforms (e.g., OpenAI, Perplexity, Anthropic) are initially analyzed to identify instances of incentive mechanisms, control mechanisms, and boundary resources.

Subsequently, additional iterations are conducted, each including at least five further platforms, to incrementally refine and extend the taxonomy.

In a third iteration, a targeted literature review is conducted to systematically incorporate novel GenAI-specific governance mechanisms, such as ex-ante moderation during content generation, e.g., stricter NSFW moderation (Cao et al., 2025). To this end, a literature corpus is constructed and analyzed through a systematic literature review (Bandara et al., 2015; vom Brocke et al., 2009; Webster & Watson, 2002).

Further iterations are then carried out until the taxonomy is stable, consistent, and complete according to predefined ending conditions.

For each iteration, the platform selection criteria, the fulfillment of ending conditions, and the corresponding adaptations of the taxonomy are systematically documented.

Literatur

• Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., & Beekhuyzen, J. (2015). Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. Communications of the Association for Information Systems, 37. doi.org/10.17705/1CAIS.03708
• Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 1–17. doi.org/10.1007/s12525-023-00680-1
• Cao, J., Zhao, K., Liu, X., Liu, C.‑W., & Duan, J. (2025). Balancing Safety, Engagement, and Compliance: Evaluating the Impact of NSFW Governance on AI-Generated Content. In Proceedings of the 46th International Conference on Information Systems, ICIS 2025, Achieving Digital Integration in the Age of AI, Nashville, TN, USA, December 14-17, 2025. aisel.aisnet.org/icis2025/ethical_is/ethical_is/9
• Chen, L., Yi, J., Li, S., & Tong, T. W. (2022). Platform Governance Design in Platform Ecosystems: Implications for Complementors’ Multihoming Decision. Journal of Management, 48(3), 630–656.
• Hein, A., Schreieck, M., Riasanow, T., Setzke, D. S., Wiesche, M., Böhm, M., & Krcmar, H. (2020). Digital platform ecosystems. Electronic Markets, 30(1), 87–98. doi.org/10.1007/s12525-019-00377-4
• Kindermann, B., Salge, T. O., Wentzel, D., Flatten, T. C., & Antons, D. (2022). Dynamic capabilities for orchestrating digital innovation ecosystems: Conceptual integration and research opportunities. Information and Organization, 32(3).
• Kundisch, D., Muntermann, J., Oberländer, A. M., Rau, D., Röglinger, M., Schoormann, T., & Szopinski, D. (2022). An Update for Taxonomy Designers. Business & Information Systems Engineering, 64(4), 421–439. doi.org/10.1007/s12599-021-00723-x
• Mayer, A. S., Kostis, A., Strich, F., & Holmström, J. (2025). Shifting Dynamics: How Generative AI as a Boundary Resource Reshapes Digital Platform Governance. Journal of Management Information Systems, 42(2), 400–430.
• Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336–359. doi.org/10.1057/ejis.2012.26
• Rietveld, J., & Schilling, M. A. (2021). Platform Competition: A Systematic and Interdisciplinary Review of the Literature. Journal of Management, 47(6), 1528–1563.
• Taeihagh, A. (2025). Governance of Generative AI. Policy and Society, 44(1), 1–22. doi.org/10.1093/polsoc/puaf001
• Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Research Commentary—Digital Infrastructures: The Missing IS Research Agenda. Information Systems Research, 21, 748–759.
• vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process. In 17th European Conference on Information Systems (ECIS 2009), Verona, Italy. aisel.aisnet.org/ecis2009/161
• Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii. www.jstor.org/stable/4132319
• Wessel, M., Adam, M., Benlian, A., Majchrzak, A., & Thies, F. (2025). Generative AI and its Transformative Value for Digital Platforms. Journal of Management Information Systems, 42(2), 346–369.

The proliferation of agentic information systems (IS), capable of autonomous decision-making, proactive task delegation, and goal-directed action, is fundamentally reshaping how organizations structure work and collaboration. Unlike traditional IS, which functioned as passive tools subject to human direction, agentic IS increasingly operate as active participants in organizational processes, creating novel human-IS constellations that challenge established assumptions about roles, responsibilities, and control. While this shift holds considerable promise for productivity and flexibility, it simultaneously introduces a range of organizational and interpersonal tensions that are poorly understood. These may include conflicts between the efficiency gains of autonomous action and the need for meaningful human oversight, between increasing reliance on agentic IS and the gradual erosion of human expertise, or between the collaborative potential of hybrid human-IS teams and the accountability requirements imposed by regulatory frameworks such as the EU AI Act. Although conceptual work has begun to map the landscape of agentic IS, empirical evidence on how these tensions manifest in real organizational contexts remains scarce, particularly from the perspective of practitioners who actively collaborate with such systems.

The goal of this thesis is therefore to empirically investigate the emerging tensions that arise when organizations integrate agentic IS into collaborative work practices. Drawing on semi-structured interviews with professionals across organizational contexts in which agentic IS are actively deployed, the study aims to surface, categorize, and theorize the tensions experienced by human actors in these settings. The findings should contribute a grounded, empirically derived framework of tensions in human-agentic IS collaboration, thereby advancing both theoretical understanding of the agentic IS paradigm and providing actionable insights for organizations navigating this transition.

Literatur

• Baird, A. & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1), 315–341. https://doi.org/10.25300/misq/2021/15882
• Fechner, P., Lämmermann, L., Lockl, J., Röglinger, M. & Urbach, N. (2025). F Toward Triadic Delegation: How Agentic IS Artifacts Affect the Patient-Doctor Relationship in Healthcare. Journal Of The Association For Information Systems, 26(6), 1703–1736. https://doi.org/10.17705/1jais.00954
• Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Information Systems Research, 33(2), 678–696. doi.org/10. 1287/isre.2021.1079
• Holldack, F., Banh, L. & Strobel, G. (2026). Agentic information systems. Electronic Markets, 36(1). https://doi.org/10.1007/s12525-025-00861-0
• Höhener, Daria (2026). The Evolution of AI Compliance Assistance from Reactive Support to Co-Agency. MIS Quarterly Executive: Vol. 25: Iss. 1, Article 4.
• Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021). Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Information Systems Research, 32(3), 713–735. doi.org 10.1287/isre.2020.0980
• Kuss, P. & Meske, C. (2025). From Entity to Relation? Agency in the Era of Artificial Intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5183049
• Leonardi, P. M. (2025). Homo agenticus in the age of agentic AI: Agency loops, power displacement, and the circulation of responsibility. Information and Organization, 35(3), 100582. https:// doi.org/10.1016/j.infoandorg.2025.100582
• Mihale-Wilson, C. A. (2025). From Whether to How Much: A Multi-tiered Perspective on Delegation to Agentic IS. ICIS 2025 Proceedings.
• Recker, J., Chatterjee, S., Sundermeier, J. & Tarafdar, M. (2025). Digital Responsibility: Current Perspectives and Future Directions. Journal Of The Association For Information Systems, 26(5), 1222–1238. doi.org/10.17705/1jais.00966
• Schuetz, S. & Venkatesh, V. (2020). Research Perspectives: The Rise of Human Machines: How Cognitive Computing Systems Challenge Assumptions of User-System Interaction. Journal Of The Association For Information Systems, 460–482. doi.org/10.17705/1jais.00608
• Stelmaszak, M., Möhlmann, M., & Sørensen, C. (2025). When Algorithms Delegate to Humans: Exploring Human-Algorithm Interaction at Uber. MISQ, 49(1), 305–330. https://doi.org/10.25300/MISQ/2024/17911
• Wissuchek, C. & Zschech, P. (2025). Challenges in Managing the Relationship Between Agentic AI Systems and Humans in Organizations. In Lecture notes in business information processing (S. 3–17). doi.org/10.1007/978-3-031-94193-1_1

The variability of platform governance strategies and the degree of platform openness play a crucial role in shaping data-sharing practices, influencing both the quantity and quality of shared data. Governance mechanisms such as resourcing and securing, as outlined by Ghazawneh and Henfridsson (2013), determine how data flows within and beyond platform boundaries. When governance emphasizes strong resourcing—such as providing well-documented APIs, incentivizing third-party contributions, and ensuring reliable infrastructure—positive network externalities emerge, encouraging increased data sharing among users and developers. However, overly restrictive governance and excessive security controls can create negative network externalities by limiting data access, reducing interoperability, and discouraging participation. The openness of a platform further shapes these dynamics; while open platforms facilitate broader data exchange and innovation, they also introduce risks related to data security, quality control, and competitive misuse.

The impact of governance variability on data-sharing practices also depends on how platforms balance openness with control. A more open, decentralized governance model, characterized by accessible data-sharing mechanisms and transparent policies, fosters network effects by enabling external contributors to enhance platform value through innovation and services, but risks a more chaotic environment. Conversely, excessively closed, centralized platforms risk stifling innovation by restricting data availability, reducing the likelihood of network effects taking hold, but allows for greater control. This seminar topic looks to explore how platforms can navigate these tensions by adopting governance approaches that optimize data-sharing practices while ensuring security, fostering trust, and sustaining long-term platform viability.

Literatur

• Boudreau, K. J. (2010). Open platform strategies and innovation: Granting access vs. devolving control. Management Science, 56(10), 1849–1872.
• Eaton, B., Elaluf-Calderwood, S., Sørensen, C., & Yoo, Y. (2015). Distributed tuning of boundary resources: The case of Apple's iOS service system. MIS Quarterly, 39(1), 217–243.
• Ghazawneh, A., & Henfridsson, O. (2013). Balancing platform control and external contribution in third‐party development: the boundary resources model. Information systems journal, 23(2), 173-192.
• Hanseth, O., & Ciborra, C. (Eds.). (2007). Risk, complexity and ICT. Edward Elgar Publishing.
• Karhu, K., Heiskala, M., Ritala, P., & Thomas, L. D. (2024). Positive, negative, and amplified network externalities in platform markets. Academy of Management Perspectives, 38(3), 349-367.
• Ofe, H., & de Reuver, M. (2024). Rethinking Openness in Data Platforms: The Impact of Data Artifact Characteristics on Platform Openness: Consequences, Scope and Mechanisms. Business & Information Systems Engineering, 1-12.
• Tiwana, A. (2013). Platform ecosystems: Aligning architecture, governance, and strategy. Newnes.
• Tiwana, A. (2015). Evolutionary competition in platform ecosystems. Information Systems Research, 26(2), 266–281.
• Wareham, J., Fox, P. B., & Giner, J. L. (2014). Technology ecosystem governance. MIS Quarterly, 38(2), 455–475.

Other potential helpful literature: 

• Levina, Olga; Mattern, Saskia; and Kiefer, Felix, "Extending Digital Platform Governance with Legal Context" (2019). AMCIS 2019 Proceedings. 4.
• Hou, H. X., Bisson, T., Leiss, S. M., Thierauf, J., Stern, A. D., Strobelt, H., ... & Lennerz, J. K. (2026). BRIDGE pilot study: a bilateral regulatory investigation of data governance and exchange. npj Digital Medicine.

Artificial intelligence (AI) can foster sustainability‑oriented change within organizations by reshaping internal processes, decision‑making, and business models to better align with environmental, social, and economic objectives. In practice, AI tools such as predictive analytics, process automation, and optimization algorithms can support more efficient resource use, lower emissions, and improved social outcomes (e.g., through ethical AI practices or equitable service delivery). However, leveraging AI for sustainability goes beyond merely deploying technology; it requires strategic alignment, cultural adaptation, and integration with organizational goals. Adapting existing governance structures, redefining workflows, and engaging stakeholders, including customers, suppliers, and regulatory partners, are central to realizing sustainable value.

At the same time, implementing AI within a sustainability context brings forth both organizational challenges, such as integrating sustainability metrics into strategic planning, managing human capital and change, and ensuring ethical deployment, and technological challenges, including data integration, algorithmic transparency, and responsible AI governance. This topic therefore investigates how organizations can use AI as part of broader sustainability strategies, how AI influences business model change aimed at sustainable value creation, and what obstacles must be overcome to ensure AI contributes positively to sustainable organizational transformation.

Literatur

• Azmat, Fara; Lim, Weng Marc; Moyeen, Abdul; Voola, Ranjit; Gupta, Girish (2023): Convergence of business, innovation, and sustainability at the tipping point of the sustainable development goals. In: Journal of Business Research 167, S. 114170. DOI: 10.1016/j.jbusres.2023.114170.
• Böttcher, Timo Phillip; Empelmann, Sarah; Weking, Jörg; Hein, Andreas; Krcmar, Helmut (2024): Digital sustainable business models: Using digital technology to integrate ecological sustainability into the core of business models. In: Information Systems Journal 34 (3), S. 736–761. DOI: 10.1111/isj.12436.
• Del Río Castro, Gema; González Fernández, María Camino; Uruburu Colsa, Ángel (2021): Unleashing the convergence amid digitalization and sustainability towards pursuing the Sustainable Development Goals (SDGs): A holistic review. In: Journal of Cleaner Production 280, S. 122204. DOI: 10.1016/j.jclepro.2020.12220.
• Martínez-Peláez, Rafael; Ochoa-Brust, Alberto; Rivera, Solange; Félix, Vanessa G.; Ostos, Rodolfo; Brito, Héctor et al. (2023): Role of Digital Transformation for Achieving Sustainability: Mediated Role of Stakeholders, Key Capabilities, and Technology. In: Sustainability 15 (14), S. 11221. DOI: 10.3390/su151411221.
• Nishant, Rohit; Kennedy, Mike; Corbett, Jacqueline (2020): Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. In: International Journal of Information Management 53, S. 102104. DOI: 10.1016/j.ijinfomgt.2020.102104.
• Pan, Shan L.; Nishant, Rohit (2023): Artificial intelligence for digital sustainability: An insight into domain-specific research and future directions. In: International Journal of Information Management 72, S. 102668. DOI: 10.1016/j.ijinfomgt.2023.102668.
• Schoormann, Thorsten; Möller, Frederik; Hoppe, Christoph; vom Brocke, Jan (2025): Digital Sustainability. In: Bus Inf Syst Eng 67 (3), S. 429–438. DOI: 10.1007/s12599-025-00937-3.
• Schoormann, Thorsten; Strobel, Gero; Möller, Frederik; Petrik, Dimitri; Zschech, Patrick (2023): Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature. In: CAIS 52, S. 199–237. DOI: 10.17705/1cais.05209.
• Watson, Richard T.; Boudreau, Marie-Claude; Chen, Adela J. (2010): Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community1. In: MIS Quarterly 34 (1), S. 23–38. DOI: 10.2307/20721413.

Electronic Performance Monitoring (EPM) refers to the use of digital tools and data to monitor and evaluate employee behavior and performance (Tomczak et al. 2018). With the rise of digitalization and process mining, organizations are increasingly able to generate detailed insights into how employees work, how processes are executed, and where inefficiencies occur (Higgins et al. 2023). While such data-driven approaches can improve efficiency, transparency, and compliance, they also raise concerns regarding privacy, fairness, and trust (Rafiei and van der Aalst 2020). For organizations, the challenge lies in balancing the benefits of monitoring with ethical, legal, and cultural considerations (Sherif et al. 2021).

The aim of this seminar paper is to empirically investigate the relationship between Electronic Performance Monitoring and Process Mining using a comparative case study design following Yin (2009). The study adopts a positivist perspective, aiming to identify patterns and differences between stakeholder groups and to derive generalizable insights. Students will apply a structured research design and conduct semi-structured interviews with two stakeholder groups, namely system designers such as developers, consultants, or project leads, and system users such as employees, analysts, or end users. The study is guided by predefined research questions and theoretical constructs, including transparency, fairness, trust, perceived control, and performance impact. Based on these constructs, students will develop an interview guideline and ensure consistent data collection across cases. The analysis follows a systematic coding procedure with predefined categories, enabling structured comparison across cases. The goal is to identify recurring patterns, similarities, and differences between designers and users regarding how EPM through process mining is designed, implemented, and perceived. In particular, the study examines the relationship between intended and perceived use of EPM systems, the factors influencing acceptance and resistance, as well as the effects of EPM on trust, motivation, and perceived monitoring. Furthermore, the study aims to derive implications for system design and organizational implementation. Students are expected to ensure methodological rigor by applying established case study principles, including clear case definition, replication logic, and structured data analysis. Access to organizations that actively use process mining is required, and students are responsible for independently recruiting suitable interview partners.

The expected outcome is a set of empirically grounded, generalizable insights on how EPM in process mining environments affects different stakeholder groups and which design and implementation factors contribute to its successful and responsible use.

Literatur

• Alder, G. S. (2001). Employee reactions to electronic performance monitoring: A consequence of organizational culture. Journal of High Technology Management Research, 12(2), 323–342.
• Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106.
• Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392.
• Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126.
• van der Aalst, W. (2016). Process Mining: Data Science in Action (2nd ed.). Springer.
• Yin, Robert k. (2009): Case Study Research: Design and Methods: SAGE

Electronic Performance Monitoring (EPM) refers to the use of digital tools and data to monitor and evaluate employee behavior and performance (Tomczak et al. 2018). With the rise of digitalization and process mining, organizations are increasingly able to generate detailed insights into how employees work, how processes are executed, and where inefficiencies occur (Higgins et al. 2023). While such data-driven approaches can improve efficiency, transparency, and compliance, they also raise concerns regarding privacy, fairness, and trust (Rafiei and van der Aalst 2020). For organizations, the challenge lies in balancing the benefits of monitoring with ethical, legal, and cultural considerations (Sherif et al. 2021).

The aim of this seminar paper is to explore how Electronic Performance Monitoring in process mining environments is perceived, interpreted, and enacted by different stakeholder groups, following an interpretive case study approach (Benbasat et al. 1987; Walsham 1995; Klein and Myers 1999). In contrast to positivist research designs, this study assumes that the meaning and impact of EPM systems are socially constructed and shaped by individual experiences, organizational context, and role-specific perspectives.

Students are expected to conduct qualitative, semi-structured interviews with two stakeholder groups, namely system designers, such as developers, consultants, or project leads, and system users, such as employees, analysts, or end users. The research design is intentionally flexible and exploratory, allowing relevant themes to emerge from the data rather than imposing a strictly predefined structure. The interviews should capture how participants make sense of EPM systems, how they interpret their purpose and use, and how they experience aspects such as transparency, control, fairness, and potential tensions between efficiency and surveillance in their everyday work context. The analysis follows an inductive, interpretive approach, for example, drawing on elements of grounded theory. The objective is not to test predefined relationships, but to develop a rich, context-sensitive understanding of how EPM is experienced and negotiated in practice. Particular attention should be paid to differences in how system designers and users construct and articulate their perspectives, as well as to the underlying assumptions, concerns, and narratives that shape these interpretations. Furthermore, the organizational context in which these systems are embedded should be considered, as it plays a critical role in how monitoring practices are perceived, legitimized, or resisted. Students are expected to reflect on their role in the research process and to ensure transparency in how interpretations are developed, following established principles for interpretive research in Information Systems. Access to organizations that actively use process mining is required, and students are expected to independently recruit suitable interview partners.

The expected outcome is a theory-building, interpretive account of how EPM in process mining contexts is understood and enacted by different stakeholders, providing nuanced insights for the design and responsible use of such systems in organizational practice.

Literatur

• Alder, G. S. (2001). Employee reactions to electronic performance monitoring: A consequence of organizational culture. Journal of High Technology Management Research, 12(2), 323–342.
• Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106.
• Benbasat, I., Goldstein, D. K., Mead, M. (1987). The Case Research Strategy in Studies of Information Systems. MISQ, 11(3), 369–386.
• Klein, Heinz K.; Myers, Michael D. (1999): A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems. In MIS Quarterly (23), pp. 67–93.
• Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392.
• Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126.
• van der Aalst, W. (2016). Process Mining: Data Science in Action (2nd ed.). Springer.
• Walsham, G. (1995). Interpretive case studies in IS research. Nature and method. European Journal of Information Systems, 4(2), 74–81. 

Over the last two decades, agile practices have become a widely known project management approach and they are widely used within the software development industry (Gemino et al., 2021, p. 162). Nowadays, as most innovative products are developed in ever-changing environments with high uncertainties, agile approaches seem promising as they offer to help with such challenges and high-uncertainty project environments (Bergmann & Karwowski, 2019, pp. 410–411). And while agile practices are grounded in software development, these practices can also be applied to other industry sectors and domains (Ciric et al., 2019, p. 1413). Furthermore, agile practices not only promise organizations to become better at navigating these high-uncertainty environments but also indicate general improvements that enable organizations to communicate more efficiently on an internal level as well as improve external communication (Ciric et al., 2019, p. 1408; Pikkarainen et al., 2008, p. 332). Some research also suggests that these practices can lead to beneficial outcomes such as an increase in performance or well-being but other research also suggests that they can actually hurt the project’s outcome under specific circumstances (Koch et al., 2023, p. 679).

Thus, while positive effects of agile practices can be observed in some projects and development teams, it is not entirely clear which agile practices have an actual measurable effect on the success of teams or projects. Some research suggests that agile projects generally have a larger success rate than traditionally managed projects (Dong et al., 2024, p. 669). However, Niederman et al. (2018, pp. 4–5) mention that while a large number of studies has been published surrounding the topic of agile practices, the empirical evidence showing successful use of these practices can often be of insufficient validity or contain a high level of uncertainty. In fact, even the term ‘Agile Project Management’ is not consistently used and surrounded by ambiguity (Dong et al., 2024, p. 669).

One of the most popular agile frameworks is the Scrum framework which introduces the following five ceremonies: The ‘Sprint’, which describes one entire iteration, the ‘Daily Scrum’, the ‘Sprint Review’, the ‘Sprint Retrospective’ and the ‘Sprint Planning’ (Verwijs & Russo, 2023, pp. 4–5). For the purpose of this seminar paper, the concept of such recurring events or ‘agile ceremonies’ is used as an anchor point to study agile success and team effectiveness.

As part of this seminar paper, a quantitative investigation (survey-based) will be conducted to investigate the relationship between agile ceremonies and team effectiveness and project success. The survey design will be provided, whereas the major task is to recruit organizations and participants for the study and analyze the data (e.g., using Structural Equation Modeling).

Literatur

• Bergmann, T., & Karwowski, W. (2019). Agile Project Management and Project Success: A Literature Review. In J. I. Kantola, S. Nazir, & T. Barath (Eds.), Advances in Intelligent Systems and Computing. Advances in Human Factors, Business Management and Society (Vol. 783, pp. 405–414). Springer International Publishing.
• Ciric, D., Lalic, B., Gracanin, D., Tasic, N., Delic, M., & Medic, N. (2019). Agile vs. Traditional Approach in Project Management: Strategies, Challenges and Reasons to Introduce Agile. Procedia Manufacturing, 39, 1407–1414.
• Dong, H., Dacre, N., Baxter, D., & Ceylan, S. (2024). What is Agile Project Management? Developing a New Definition Following a Systematic Literature Review. Project Management Journal, 55(6), 668–688.
• Ergün, A., Betteldorf, L., & Plattfaut, R. (2025). The Impact of Daily Stand-Up Meetings on Project Success & Team Dynamics. AMCIS 2025 Proceedings.
• Koch, J., Drazic, I., & Schermuly, C. C. (2023). The affective, behavioural and cognitive outcomes of agile project management: A preliminary meta‑analysis. Journal of Occupational and Organizational Psychology, 96(3), 678–706.
• Niederman, F., Lechler, T., & Petit, Y. (2018). A Research Agenda for Extending Agile Practices In Software Development and Additional Task Domains. Project Management Journal, 49(6), 3–17.
• Pikkarainen, M., Haikara, J., Salo, O., Abrahamsson, P., & Still, J. (2008). The impact of agile practices on communication in software development. Empirical Software Engineering, 13(3), 303–337.
• Verwijs, C., & Russo, D. (2023). A Theory of Scrum Team Effectiveness. ACM Transactions on Software Engineering and Methodology, 32(3), 1–51.